Tech and the City – AI in Financial Services – key insights from a private industry roundtable
As part of the Tech and the City – AI in Financial Services half-day summit, techUK and TheCityUK convened a private roundtable bringing together C-suite and Director level industry leaders across financial institutions, technology providers, consultancies, and data and AI specialists. The discussion explored the opportunities presented by AI, the challenges of AI adoption, and how industry, policymakers, and regulators can work together to enable safe and trusted innovation.
This note captures the key themes discussed. It is non-attributable and reflects collective insights.
The opportunity: AI as a strategic capability
Participants were optimistic about the transformative potential of AI in financial services over the next several years. There was a shared view that technological capability is beginning to align with ambition, enabling gains in productivity, customer outcomes, risk management, and cost efficiency.
Participants stressed that AI should not be treated as a single technology or trend. Instead, firms should take a holistic view, recognising the complementary roles of traditional AI, machine learning, and generative AI across different business functions and risk profiles. The opportunity lies not only in frontier innovation, but in systematic and scaled deployment across the enterprise.
From a consumer and SME perspective, AI was seen as having the potential to improve access to financial guidance and decision-making, supporting financial inclusion and financial literacy. However, participants were clear that realising these benefits will depend on trust, transparency, and appropriate safeguards.
Regulation: enabling innovation while managing risk
Regulation featured prominently in the discussion, with broad agreement that UK regulation is not currently a barrier to AI adoption in financial services. Participants welcomed the UK’s principles-based approach, viewing it as more flexible and better suited to fast-moving technologies than more prescriptive regimes.
However, a consistent theme was the gap between regulatory intent and internal interpretation. In practice, firms’ approaches to AI are shaped by risk appetite, governance structures, and concerns about scrutiny. This can lead to cautious decision-making, particularly for customer-facing use cases, even where regulatory frameworks are permissive.
Participants noted that boards and senior executives increasingly recognise AI as a strategic competitiveness issue, but that belief can diminish further down organisations where implementation decisions are made. Clearer supervisory signals, shared industry understanding of ‘what good looks like’, and continued dialogue between regulators and industry were seen as critical to bridging this gap.
Operational resilience as the critical constraint
Operational resilience emerged as one of the most significant challenges to scaling AI safely and sustainably. Participants agreed that existing approaches to resilience, risk management, and enterprise architecture are being stretched by the pace, complexity, and interconnected nature of AI adoption.
As firms move from isolated pilots to enterprise-wide deployment, they face growing challenges around explainability, model governance, data lineage, and incident management. Managing large numbers of AI-enabled components — often developed across different teams and platforms — raises questions about visibility, accountability, and systemic risk.
Several participants emphasised that operational resilience must be addressed end-to-end, linking business processes, technology, data, and infrastructure. This includes investing in AI capabilities within technology and control functions themselves, such as monitoring, testing, and incident response, rather than focusing solely on front-line use cases.
Without this foundation, there is a risk that today’s AI solutions become tomorrow’s legacy systems, constraining innovation rather than enabling it.
From experimentation to enterprise integration
While experimentation with AI is widespread, participants highlighted a clear shift towards embedding AI within core transformation and change programmes. Many firms are moving beyond proofs of concept and asking how AI should shape enterprise architecture, operating models, and long-term investment priorities.
However, this transition is taking place against a backdrop of limited change capacity, with significant resources tied up in maintaining legacy systems and meeting regulatory obligations. Participants stressed the importance of prioritisation, ensuring AI initiatives are aligned with defined business outcomes and integrated into existing governance and investment processes.
There was broad agreement that bottom-up innovation — including “citizen development” — can unlock value and insight, but only if supported by clear frameworks, controls, and escalation paths appropriate to a regulated environment.
Policy implications
The roundtable highlighted several implications for public policy as AI adoption in financial services accelerates:
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Operational resilience is becoming a policy-critical issue for AI at scale. As AI systems are embedded across business processes, resilience frameworks will need to consider not just infrastructure, but interconnected models, data flows, and decision-making systems.
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Skills and adoption are as important as technical capability. Policy efforts to support AI adoption should continue to focus on workforce skills, AI literacy, and responsible deployment, alongside investment in infrastructure and innovation.
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Principles-based regulation remains well suited to fast-moving technologies. Participants broadly supported the UK’s current approach, noting that flexibility and outcomes-based expectations are critical given the pace of change in AI.
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Collaboration between industry and regulators will be essential. Shared understanding of emerging use cases, risks, and mitigations will help ensure policy remains proportionate and aligned with real-world deployment.
What this means for regulators
From an industry perspective, the discussion suggested several areas where regulatory engagement could further support safe and effective AI adoption:
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Clear supervisory signals on acceptable risk and ‘what good looks like’. While firms generally understand their legal and regulatory obligations, uncertainty arises in interpretation and application. Practical examples and shared expectations could reduce unnecessary caution.
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Continued focus on outcomes rather than technology-specific rules. Participants emphasised the importance of avoiding overly prescriptive approaches that may struggle to keep pace with technological change or unintentionally constrain innovation.
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Alignment between AI governance and existing operational resilience frameworks. Firms are increasingly seeking to integrate AI risk management into established resilience, model risk, and incident management practices, rather than treating AI as a standalone risk domain.
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Ongoing dialogue as business models evolve. As AI blurs traditional boundaries between financial services activities, continued engagement will be needed to ensure regulatory frameworks remain aligned with how services are delivered and consumed.
Looking ahead
The roundtable underscored both the scale of the opportunity and the complexity of the challenge facing financial services as AI becomes embedded across the sector.
Realising the benefits of AI will require sustained collaboration between industry, policymakers, and regulators, underpinned by strong governance, investment in operational resilience, and a shared understanding of acceptable risk.
techUK and TheCityUK will continue to work with members and stakeholders to support this agenda, promoting collaboration that enables safe, trusted, and competitive innovation in AI across financial services.
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Kir Nuthi
Kir Nuthi is the Head of AI and Data at techUK.
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